But one of the most notable contracts was a 5-year, $800 million contract with Booz Allen Hamilton to support the Joint AI Center (JAIC) warfighting group. (Another contract to create the Joint Common Foundation, a cloud-based AI development environment for the military, is also in the works now.) This week, the JAIC also shared more details this week about Project Salus, a series of predictions from 40 models about supply chain trends in the age of COVID-19 for the U.S. Northern Command and National Guard. With Salus, the JAIC went from idea to AI for military decision makers in two months.

The JAIC was created in 2018 to lead Pentagon efforts to use more artificial intelligence, and is also tasked with leading military ethics initiatives. Perhaps more than any time since its creation, the JAIC is in the midst of change and transition. JAIC director and Air Force Lt. Gen. Jack Shanahan, who has served as JAIC director since its founding steps down next week. VentureBeat spoke at length with his incoming replacement, current JAIC CTO Nand Mulchandani about the future of the organization.

Although Shanahan retires Aug. 1 according to the Air Force website, JAIC CTO Nand Mulchandani will take over as acting director in the next two weeks. Unlike Shanahan, Mulchandani spent most of his career building startups in Silicon Valley.

In March, the JAIC finished revamping how it approaches AI projects and workflows and moved to adopt a different organizational structure; Mulchandani said the restructure makes the JAIC the only organization in the Pentagon with product managers and an approach to AI product development that resembles Silicon Valley startups and enterprise sales teams.

In an interview with VentureBeat, Mulchandani talked in detail about the restructure of the JAIC that took place in March, how JAIC is helping the U.S. military develop predictive early warning systems, how the Joint AI Center will use Google Cloud’s Anthos and the $800 million Booz Allen Hamilton contract, and how COVID-19 is influencing the group’s operating mission.

This interview was edited for brevity and clarity.

VentureBeat: How will the Booz Allen Hamilton contract play a role in supporting JAIC teams? And will the Joint Common Foundation (JCF) or any other part of JAIC be part of the Google Cloud Anthos deal that was signed with the Pentagon earlier this week?

Mulchandani: We had to find somebody who could help us pull together the sort of software-hardware interface and bring that level of expertise in terms of dealing with the tactical edge or applying models to different form factors with drones and things like that. And that’s where the [Booz Allen Hamilton] joint warfighting contract came in for dealing with incredibly complex deployments across geographic regions, and dealing with different pieces of hardware, software, etc.

So that’s what we’re relying on them for. Their job really is to work with us on pulling in the best AI technology to be able to deploy, so in some sense, we’re not relying on Booz to actually build out all the artificial intelligence for us. They’re here to help us assemble and pull the best of these fits together.

Similarly, we have a Joint Common Foundation RFP out of the street that we’re actually going through the procurement process right now. We’ll have news on that hopefully soon.

The JCF is different in that it is a more of an AI development environment as opposed to an infrastructure management system. The basic goal is when we build out the JCF one day hopefully very soon, we will bring best of breed AI tools and products into the system to allow for AI development.

VentureBeat: So will JAIC be using Anthos then?

Mulchandani: That one [Anthos] came in from the Defense Innovation Unit (DIU). And the sort of multi-cloud model I mean, it’s tied up in this whole discussion around JEDI and cloud infrastructure and the JCF. The DIU did that particular one, and they’ve done it as a sort of general purpose contract that allows DoD customers to use Anthos as a multi-cloud management system.

The JCF is going to be fairly agnostic towards the infrastructure level stuff. We’ll support pretty much all the standard stuff that will support Google Cloud, Azure, Amazon — these are all targets for workloads. And when JEDI shows up, the JCF will be pretty agnostic towards all types of cloud infrastructure and targets. So it’s somewhat related, but those are kind of two separate things on that front.

VentureBeat: Can you walk me through some of the structural changes that the JAIC through earlier this year?

Mulchandani: The reorientation of JAIC basically came around the need for product managers and product teams that build world-class products, and we need a missions team with colonels from our military running those missions that understand to a very deep level what our needs and requirements are.

What we’ve done is taken two sort of key models and applied them. So one is the venture capital model, which is really more about how…we approach investing in and selecting the products and projects that we take on. And the other I would characterize as an enterprise sales model.

Those are the two models that we apply, because they are well understood. Thousands of companies do this every day out in industry in the world.

I spent 26 years in Silicon Valley before going to the DoD. I’ve been here one year, and the most natural pattern for me was the venture capital model because it deals with early stage technology. At the DoD that’s a little trickier, because this is the United States military, and we have lots of people who are trained in doing military things, not building software.

If you’ve ever been in an enterprise technology company, how we build and run sales teams and deal with volumes of customers, and how you triage them and grow that customer base to convert them from leads to customers. “Sales” could have a negative connotation, like we’re trying to sell something, but the way we’ve modeled it is more in terms of customer relationships and customer knowledge.

So we now have a product group that’s composed of a couple of key types of people. So number one is the product manager. The product manager is in charge of owning that sort of requirements-gathering, functional specs, [and] selecting and working with the engineering team to build this thing out and make it happen.

We’ve got a very robust AI and data science team. We’ve got a test and eval section testing products to make sure that they’re there, but it’s also imbibing and making sure that we’re following many of the ethical principles. And the missions team is really — you should think of [that] as our enterprise sales team.

VentureBeat:How is ProjectSalus a step in a new direction for JAIC?

Mulchandani: So what we did with Salus was a great example of this model, where instead of spending a year over-specifying the product, you get the core needs and requirements in a really basic depth. The funny part was the first version of Salus that we showed NORTHCOMM and the [National] Guard; in some sense, they were a little puzzled because they were like “What’s this? This seems like really, really half baked.” And we had to explain to them that this is the new world, right, that in seven days or eight days this is what you typically get.

It’s a bare bones product that barely works. Every single company that I started, you know, the first board meeting after we raised a couple million dollars from investors, you go show the first product, the “Hello World” product to the board, and if they haven’t done venture capital before or early tech, some of them do fall out of their chairs and say, “Wait, I just spent a couple of million bucks for this company and this is what you built?” It’s like, well no, it’s not ready yet. We’re going to stay close to you and learn what you want and what you need, and we’re going to iterate with you.

VentureBeat: Howis COVID-19 sort of changing the way that JAIC looks at its mission, or things of that nature?

Mulchandani: What [Salus] actually taught us to do was to get this sort of concept of code thing into practice, this idea of having the product manager, project managers, the legal and policy folks, the test and eval, everyone; co-residents, the missions team, all aligned and working together. We basically want to bottle this up this experience and this method of building products, and we want to replicate it.

This all stemmed a lot from an off-site that we did a couple months ago. And it gave us a chance for the entire JAIC to actually go off site, sit together, think, ideate, spend some time together outside of the office. And one of the things that struck us was every great organization has a business model and a business plan that it operates on.

When you start a company. What is your world-changing idea? What’s your business plan and model to go attack that business plan? That led us to our mission and charter around leverage and creating repeatability and industrialization and scale around AI.

VentureBeat:In an interview earlier this week,Lt. Gen. Shanahan brought up this notion of a national predictive warning system and that JAIC will be involved with more of that in the future, in part to respond to COVID-19 and as part of a general effort to make the DoD more predictive than reactive. Can you talk a little bit more about that idea of creating early warning systems with AI?

Mulchandani: Typically in the popular imagination, AI always immediately folks go to killer robot and Terminator immediately. But what you’re going to realize is the biggest revolution that’s going to occur with AI in the short term, and is already happening, is in decision support.

That is the biggest and most critical area where it can aid and unambiguously support, and [it’s] also easily deployable without any of the sort of ethics and other issues that we’re grappling with longer-term around the economy and things like that.

What makes AI so powerful — and this is where Lt. General Shanahan, and all of us are sort of pushing this idea of this area in AI — is the ability to highlight and come up with a non-obvious result. When you look at many of the more surprising results in AI recently — all the stuff around Go for instance — the one phrase that we all have to look at is the one which says, the computer behaved in a non-human way. That to me was the critical insight into that whole drama — the non-obvious nature of the solution that the system came up with. And that typifies AI, and that’s where it’s different from statistics and the normal math that people use.

It’s the non-obvious things that humans won’t think about, which is where we’re going with predictive capabilities, and we want to apply this sort of model, not only to pandemic modeling, but of course, we’re going to apply this to … joint warfighting and many of the decision support stuff that we need at the DoD to be able to fight and win the next war if and when it happens.